Machine Learning Enables the Future Network

Posted on: 31st October 2016

A new report from ABI Research suggests that telcos are expanding their plans for using machine learning. Specifically, exploring ways that machine learning can make network operations more intelligent, automated and efficient.

Much of the focus on AI and Machine Learning in telecom is on customer-facing functions such as support and service queries. But ABI suggests that there is more fundamental impact on the way.

According to ABI, “mobile operators will devote more than $50 billion to big data and machine learning analytics through 2021.” More significantly: “Machine learning technologies will lead operators to profoundly change how they manage the telecom business.”

And the activity of large established vendors suggests this is not a niche activity: “Leading infrastructure vendors are delivering big data and machine learning solutions oriented toward network operations.”

Three Generations of Analytics

ABI’s research notes the progression from “descriptive” to “predictive” analytics. But from our discussions, visionary telcos and web-scale companies are already plugging machine learning into a third generation of their analytics strategies: “prescriptive” analytics. That application is key to achieving the speed and agility that all are striving for.

It’s a logical enough progression, and often referred to. Any variant of “turning data into insight, and insight into action” expresses it clearly enough.

However, the challenge is that once you move from looking at data as data, to acting in the real world, you’re into the specifics of how the business works. And in a network-based business, that’s complex – but very, very worth it.

There’s a world of difference between knowingthat 70% of your network capacity isn’t being used and knowing how to reduce that to 20%. The value of the first is only measurable in the time saved to calculate the information. But the value of the second is multiple percentage points on overall capex and opex.

Let’s take a few examples:

Descript-ive

Predict-ive

Prescript-ive

What happened?

What’s going to happen?

What action should I take?

Did service quality fall below required levels last month?

Is service quality likely to fall below contracted levels in the next month?

What network configuration changes are required to avoid drop-offs in service quality?

Is a DDOS attack taking place?

Will a DDOS attack take place?

How do I reconfigure my network to neutralize a DDoS attack?

How AI is used is different between each application type:

Descript-ive

Predict-ive

Prescript-ive

Data Analysis, Pattern Recognition

Forecasting

Automation & Optimization

Generic IT Platform

Generic statistical modelling techniques

Network design techniques

Telcos and web scale players alike look forward to more autonomous networks, and more agile businesses. Machine learning, applied to their networks, is an integral enabling technology for achieving that.

ABI’s report summarises the vision nicely: “In just a few years, we will see the mobile networks of tomorrow manifest into giant, distributed supercomputers, with radios attached, continuously re-engineered by machine learning.”

For real world examples of how Machine Learning and AI have been applied to solve network problems, click here.